Analyzing the Role of Community and Individual Factors in LAMP Grant Funding: Identifying Diverse Barriers Across Clustered US Counties
FAS Food Systems Impact Fellowship Capstone Project, April 2024
Introduction
Local Agriculture Market Program (LAMP)
The USDA’s Agricultural Marketing Service (AMS) administers a variety of grant programs aimed at strengthening local and regional food systems. The Local Agriculture Market Program (LAMP) is one such program that supports direct producer-to-consumer marketing, food enterprises, and value-added agricultural products. Established under the 2018 Farm Bill, LAMP fosters community collaboration and public-private partnerships to improve regional food economies, aiding in the development of business strategies and infrastructure for local food systems. The Farm Bill provided LAMP $50 million per year in mandatory funding and the programs received significant supplemental funding through the Consolidated Appropriations Act of 2021 and the American Rescue Plan of 2021.1 The major grant programs within LAMP include the Local Food Promotion Program (LFPP), Regional Food Systems Partnership (RFSP), and the Farmers Market Promotion Program (FMPP).
Building community capital through food systems investment
Allocating grant funding
The goals of the LAMP program include: (1) simplify the application processes and the reporting processes for the Program; (2) improve income and economic opportunities for producers and food businesses through job creation; and (3) strengthen capacity and regional food system development through community collaboration and expansion of mid-tier value chains.2
Each program within LAMP includes a set of constraints intended to improve the allocation of resources to specific program activity areas.
In 2021, AMS partnered with Florida A&M University and the University of Maryland Eastern Shore on a project focusing on the following goals3:
Evaluate barriers to AMS grant opportunities for socially disadvantaged communities
Invest in building trust and confidence between these communities and the USDA
Take action to rectify inequalities in program access through targeted outreach, training, and technical assistance.
The results of this work are intended to be used to improve access and reduce barriers for all applicants, presumably part of the agency’s renewed efforts to address USDA’s history of systemic discrimination.4
Community preparedness
Recent research suggests that the success of food system interventions, policies, and strategies for local economic development may hinge on the preexisting levels of community capital.5
Additional research showed positive associations between cultural and social capital and farm to school activity.6
Much of this research highlights community assets that are often overlooked in community development work.7
Objective
This report intends to lay the groundwork for an analytic approach that helps determine which community characteristics are associated with LAMP grant funding allocation. This could help determine if there is something akin to a “threshold of community preparedness” the unknowingly results in certain low-resource communities being excluded from LAMP programming. If so, the results of this research could provide insight into the particular characteristics associated with LAMP access, which could help agency staff to better allocate resources to ensure equitable access to grant funds.
Methods
Data access and aggregation
As a first step, a variety of data sets were obtained, cleaned, organized, and used for general data exploration. Information on specific datasets and sources can be found below. All work was done using the open source statistical software R version 4.4.0.8
LAMP grant data
Information on LAMP awards came from the LAMP Navigator website, where AMS has made this information publicly available, along with a dashboard for sorting, filtering, and visualizing the grant information.9 Along with information about the organizations receiving the grant, the dataset includes information on the purpose of the grant (e.g., technical assistance, infrastructure, processing), the match amount, and the total project cost.
LAMP grant award amounts, 2006 - 2023
Each green dash represents a single grant award
Geographic distribution of LAMP Grants, 2006-2023
Community characteristics
A variety of socioeconomic and environmental factors were investigated to assess how they may influence the likelihood of receiving a LAMP grant. These factors include indicators of community wealth, which encompasses social capital, natural capital, financial capital, and a variety of other forms of wealth, which have been shown impacts the ability to engage and participate in such programs.10 Additionally, it includes factors related to poverty and food security, which have been shown to exacerbate vulnerabilities and influence accessibility and participation in programs.11 Finally, considering the food systems-focus of LAMP, factors related to urbanization and proximity to agricultural land were included because they can influence market dynamics and food system connectivity.12
Indicators of community wealth
Community wealth data were accessed via the USDA AMS Data and Metrics GitHub repository.13 The main source of data was the “Indicators of Community Wealth” dataset within this repository, which was the result of various pre-processing steps that are outlined within the Rmarkdown file included in the repo.
| Indicators of community wealth variables | ||
|---|---|---|
| Descriptions and sources of data used in analysis | ||
| Description | Data Source | |
| Uncategorized | ||
| health_factors | Z-scores that represent potential future health conditions | Derived from national health statistics |
| health_outcomes | Z-scores that represent physical health with respect to today’s health | Derived from national health statistics |
| Food Access | ||
| food_secure | Percentage of the population defined as food secure | Derived from USDA data |
| Processing & Distribution | ||
| foodbev_est_CBP | TBD | TBD |
| est_CBP | TBD | TBD |
| Community Characteristics | ||
| highway_km | Proximity to interstate highways | Derived from federal highway data |
| broad_16 | Access to fixed advanced telecommunications, i.e., high-speed internet access | FCC data |
| pc1b_manufacturing | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| pc2b_infrastructure | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| create_indus | Number of creative industry businesses per 100,000 people | Derived from NAICS data |
| racial_div | Racial/ethnic diversity index based on six ethnic categories tracked by the U.S. Census | U.S. Census data |
| pub_lib | Number of public libraries per 100,000 people | Derived from NAICS 519120 |
| create_jobs | Percentage of workers employed in the arts | Derived from NAICS 7111 and 7113–7115 |
| museums | Number of museums per 100,000 people | Derived from NAICS 712110 |
| pc1c_artsdiversity | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| pc2c_creativeindustries | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| localgovfin | County government cash and security holdings net of government debt per capita | Derived from local government financial reports |
| owner_occupied | Number of owner-occupied units without a mortgage per capita | U.S. Census data |
| deposits | Level of deposits to FDIC-insured institutions per capita | FDIC data |
| pc1f | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| ed_attain | Percentage of the adult population with a bachelor’s, graduate, or professional degree | U.S. Census data |
| insured | Percentage of the population having health insurance | Derived from U.S. Census data |
| primary_care | Number of primary care physicians per 10,000 people | Medical association data |
| pc1h_healtheducation | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| pc2h_medicalfoodsecurity | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| natamen_scale | Natural Amenities Scale (NAS) designations | USDA Economic Research Service |
| prime_farmland | Percentage of acres defined as prime farmland | National Agricultural Statistics Service |
| conserve_acre | % of all acres under conservation easement, 2016 | NCED (2016) |
| acre_FSA | % of total acres in conservation programs and woodlands, 2017 | USDA FSA (2017) |
| acre_NFS | Percent of total acres in National Forests, 2017 | USFS (2017) |
| pc1n_naturalamenitiesconservation | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| pc2n_farmland | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| pvote | % of eligible voters that voted, 2012 | Rupasingha et al., (2006) |
| nccs | Number of nonprofit organizations per 1,000 population, 2014 | Rupasingha et al., (2006) |
| assn | Social establishments per 1,000 people, 2014 | Rupasingha et al., (2006) |
| respn | % response rate to U.S. Population Census, 2010 | Rupasingha et al., (2006) |
| pc1s_nonprofitsocialindustries | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |
| pc2s_publicvoiceparticipation | Principal component derived from Schmit et al. 2021 | Schmit et al. 2021 |